KREDI KARTI MÜŞTERILERININ KMEANS ALGORITMASI ILE SEGMENTASYONU: BIR VERI MADENCILIĞI UYGULAMASI

Öz

Günümüz bankacılık sektöründe müşteri odaklı stratejiler, sürdürülebilir rekabet avantajı elde etmenin temel unsuru haline gelmiştir. Bu çalışmada, kredi kartı müşterilerinin finansal davranışlarına göre segmentasyonu gerçekleştirilmiştir. Araştırmada Kaggle veri havuzundan elde edilen ve 8950 aktif kredi kartı kullanıcısının 6 aylık finansal hareketlerini içeren veri seti kullanılmıştır. Veri madenciliği sürecinde CRISP-DM metodolojisi temel alınmış, analizler Orange Data Mining yazılımı ile gerçekleştirilmiştir. Eksik veriler ortalama ile doldurulmuş ve değişkenler Z-skor normalizasyonu ile standartlaştırılmıştır. K-Means kümeleme algoritması uygulanarak müşteriler 4 farklı segmente ayrılmıştır: Pasif Müşteriler, Riskli Borçlular, VIP/Sadık Müşteriler ve Düşük Profilli Müşteriler. Kümeleme kalitesi Silhouette analizi ile değerlendirilmiş ve makul düzeyde bir küme yapısı elde edilmiştir. Bulgular, müşteri tabanının homojen olmadığını ve risk ile karlılık açısından birbirinden farklı alt gruplardan oluştuğunu ortaya koymuştur. Çalışma, bankaların veriye dayalı kişiselleştirilmiş pazarlama stratejileri geliştirmelerine katkı sağlamaktadır.

Anahtar Kelimeler

• Müşteri segmentasyonu
• K-Means kümeleme
• Kredi kartı
• Veri madenciliği
• Bankacılık

SEGMENTATİON OF CREDİT CARD CUSTOMERS USİNG THE KMEANS ALGORİTHM: A DATA MİNİNG APPLİCATİON

Öz

In today's banking sector, customer-oriented strategies have become a fundamental element for achieving sustainable competitive advantage. This study aims to segment credit card customers based on their financial behaviors. The dataset used in the research was obtained from the Kaggle repository and contains 6-month financial transactions of 8950 active credit card users. The CRISP-DM methodology was adopted in the data mining process, and analyses were conducted using Orange Data Mining software. Missing values were imputed with mean values, and variables were standardized using Z-score normalization. By applying the K-Means clustering algorithm, customers were divided into 4 distinct segments: Passive Customers, Risky Borrowers, VIP/Loyal Customers, and Low-Profile Customers. The clustering quality was evaluated using Silhouette analysis, yielding a reasonable cluster structure. The findings reveal that the customer base is not homogeneous but consists of subgroups that differ significantly in terms of risk and profitability. This study contributes to the development of data-driven personalized marketing strategies for banks.

Anahtar Kelimeler

• Customer segmentation
• K-Means clustering
• Credit card
• Data mining
• Banking.

Kaynakça

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